COSAR: hybrid reasoning for context-aware activity recognition

By Daniele Riboni, Claudio Bettini. Personal and Ubiquitous Computing, 2011

312 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/14fca7352c81c9401aef4eaf095e80ef8b74c57a

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches (2016, background): “The increasing popularity of smartphones and smartwatches, and the advances in their sensing and computing power, promotes using them as convenient and natural agents for context recognition systems [6], [18], [19], [20], [21], [22], [24], [26], [27], [31], [32].”
Transition-Aware Human Activity Recognition Using Smartphones (2016, methodology): “One example of this is also found in [36] where a method called statistical-hist was proposed.”
A Survey on Activity Detection and Classification Using Wearable Sensors (2017, background): “There are yet other sensor combinations explored in [136], [139], [146], [152], [153], and [221].”
A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time (2017, methodology): “Such algorithms include neural networks and linear or non-linear discriminant learning [76], expressive description logics (DLs) [88], possibly coupled with rule-based languages [89,90], etc.”
The Semantic Web (2011, unknown): “[4] and other quality dimensions like completeness [2] provide a characterization of existing links), they do not allow for assessing how many new things might be made discoverable thanks to the links (ii).”
Advanced internet of things for personalised healthcare systems: A survey (2017, background): “COASR [118] is the typical case of combining two approaches in ADL detection for self-management of elder people at their own homes.”
A Survey on Human Activity Recognition using Wearable Sensors (2013, background): “Another approach is to carry out feature extraction and classification in the integration device, so that raw signals would not have to be continuously sent to the server [31], [48].”